US20140203813A1 - Method of estimating the state of charge of an electric battery - Google Patents

Method of estimating the state of charge of an electric battery Download PDF

Info

Publication number
US20140203813A1
US20140203813A1 US14/119,126 US201214119126A US2014203813A1 US 20140203813 A1 US20140203813 A1 US 20140203813A1 US 201214119126 A US201214119126 A US 201214119126A US 2014203813 A1 US2014203813 A1 US 2014203813A1
Authority
US
United States
Prior art keywords
cell
state
charge
battery
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US14/119,126
Other languages
English (en)
Inventor
Ana-Lucia Driemeyer-Franco
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Renault SAS
Original Assignee
Renault SAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Renault SAS filed Critical Renault SAS
Assigned to RENAULT S.A.S. reassignment RENAULT S.A.S. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DRIEMEYER-FRANCO, Ana-Lucia
Publication of US20140203813A1 publication Critical patent/US20140203813A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G01R31/3658—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/18—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules
    • B60L58/21—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules having the same nominal voltage
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B60L2240/545—Temperature
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/44—Control modes by parameter estimation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Definitions

  • the present invention relates to the field of electric batteries comprising a plurality of electric accumulators.
  • It relates more particularly to a method of estimating the state of charge of such an electric battery.
  • This invention has a particularly advantageous application for estimating the state of charge of an electric traction battery of a motor vehicle.
  • the state of charge of an electric battery is estimated as a function of measurements relative to the whole of the battery, for example as a function of the voltage measured across the terminals of the battery, of the current passing through the battery and/or of the temperature of the battery.
  • an electric battery generally comprises several electric accumulators called cells which have characteristics that are different from each other, such as for example the variation of their capacity and of their internal resistance.
  • the range of use of the battery is imposed by the most charged cell and by the least charged cell.
  • the least charged cell of the battery will reach a state of zero charge, that is to say completely discharged, before the other cells are completely discharged.
  • the battery as a whole will therefore be considered as discharged, whereas some of the cells are not completely discharged.
  • the range of use of the battery is therefore limited by the deviation between the most charged cell and the least charged cell.
  • the state of charge of the battery information is displayed directly on the dashboard of the vehicle so that the driver knows the autonomy of his vehicle.
  • the driver must be able to base his decisions whilst driving on reliable state of charge information.
  • An error in the estimation of the state of charge of the battery can result in causing the driver to make a poor decision and in finding himself in a disagreeable situation, for example he finds himself immobilized because of a lack of energy, or even in a dangerous situation, for example if he lacks power whilst overtaking.
  • the present invention proposes a method making it possible to provide an accurate and reliable estimation of the state of charge of a battery.
  • This method of estimating the state of charge of the battery takes account of the possible unbalance between the cells of the battery.
  • the estimated value of the state of charge of the battery indicates a maximum charge, that is to say in practice equal to 100%, when the most charged cell has a maximum state of charge, in practice equal to 100%, and indicates a minimum charge, in practice equal to 0%, when the least charged cell has a minimum state of charge, in practice equal to 0%.
  • the method according to the invention makes it possible to obtain a continuous and representative variation of the state of charge of the battery between these two extreme values.
  • said Kalman filter comprises at least one parameter depending on the functioning of the cell
  • FIG. 1 is a diagrammatic representation of the determination of the state of charge of the battery from the state of charge determined for each cell of the battery
  • FIG. 2 is a diagrammatic representation of the electric circuit modeling one of the cells of the battery
  • FIG. 3 is a diagrammatic representation of the open circuit voltage of a cell as a function of the state of charge of the cell modeled by the circuit shown in FIG. 2 (solid line), and of its approximation as a piecewise affine function (dotted line),
  • FIG. 4 is a diagrammatic representation of the use of four Kalman filters for estimating the state of charge of the cell modeled by the circuit shown in FIG. 2 each corresponding to a different affine part of the function representing the open circuit voltage of that cell as a function of the state of charge of that cell,
  • FIG. 5 shows the variation of the state of charge of the battery estimated by the method according to the invention as a function of time (solid line) and the corresponding variation of the state of charge of the cells of the battery (dotted lines), in the absence of a system for balancing the cells,
  • FIG. 6 shows the variation of the state of charge of the battery estimated by the method according to the invention as a function of time (solid line) and the corresponding variation of the state of charge of the cells of the battery (dotted lines), in the presence of a system for balancing the cells.
  • an electric battery comprising a plurality of electric accumulators connected in series and hereafter called cells is considered.
  • This electric battery is for example a traction battery of an electric or hybrid motor vehicle. It comprises any number of cells, for example equal to 96.
  • index i will indicate the magnitudes and the operators associated with the cell of index i, where i takes the values from 1 to 96.
  • the state of charge of the battery or of a cell is commonly expressed as a percentage of the maximum state of charge of that battery or of that cell.
  • a state of charge equal to 100% will therefore indicate a battery or a cell that is fully charged, and therefore in a maximum state of charge.
  • a state of charge of 0% will indicate a battery or a cell that is completely discharged, and therefore in a minimum state of charge.
  • the estimation of the state of charge of the battery is carried out at different times, preferably at regular time intervals of period Te, starting from an initial time t0.
  • the time index is omitted, the value of the variable is considered at the time k of the measurement or of the calculation in progress.
  • the method according to the invention is implemented by an electronic control unit.
  • This electronic control unit is adapted to receive information coming from sensors of the battery adapted to measure different values of voltage, current and temperature inside the battery, as indicated below.
  • the method of estimating the state of charge, referred to as SOCbatt_k, of the electric battery comprises the following steps:
  • the left hand section of FIG. 1 represents the determination of the value of the state of charge SOCcell_est_i of each cell of the battery at the time t_k considered in step a).
  • the value of the state of charge SOCcell_est_i of each cell is estimated by an observer which in this case is a Kalman filter FK_i associated with said cell of index i, at the time t_k.
  • the electronic control unit determines, at each time t_k, the state of charge of the most charged cell SOCcell_max_k and the state of charge of the least charged cell SOCcell_min_k at that time, according to the following formulae:
  • This step is represented by blocks 10 and 11 in FIG. 1 .
  • step b) the electronic control unit determines the range of use of the battery at the time t_k, equal to the predetermined maximum value of the state of charge of a cell minus the deviation between the state of charge of the most charged cell SOCcell_max_k and the state of charge of the least charged cell SOCcell_min_k.
  • the predetermined maximum value of the state of charge of any cell is for example fixed and equal to 100%.
  • Block 12 in FIG. 1 carries out the calculation of this range of use.
  • step c) the electronic control unit determines the state of charge of the battery SOCbatt_k at the time t_k by calculating the ratio between:
  • This operation is carried out by block 13 of FIG. 1 .
  • the state of charge SOCbatt of the battery is determined according to the formula:
  • SOCbatt — k SOCcell_min — k /(1 ⁇ (SOCcell_max — k ⁇ SOCcell_min — k ).
  • SOCcell_est_i of each cell of the battery at the time t_k in question can be carried out in different ways.
  • step a the following steps are carried out for each cell of the battery:
  • step a3) there is preferably also determined a temperature temp_cell_est_i of the cell and the charge of the cell as a function of this temperature is estimated in step a3).
  • said input variable comprises at least the current i_cell passing through the cell.
  • this variable is identical to the current I bat passing through the battery itself because, as the cells are connected together in series, the current passing through the battery is equal to the current passing through each cell.
  • the current i_cell corresponds to the current I bat to which is added the balancing current of the cell because this balancing system is connected in parallel with the cell.
  • Said output variable comprises at least the voltage u_cell across the terminals of the cell.
  • These input and output variables are determined for each cell of index i at each time t_k. They are preferably measured.
  • the state variable of which it is sought to determine the values is therefore calculated at each time step not only with the help of the measured input variable but also as a function of a correction parameter derived from the output variable.
  • each cell is modeled by an electric model circuit 100 .
  • An example of such an electric model circuit 100 is shown in FIG. 2 .
  • a voltage generator 101 generating a voltage OCV, a resistor 102 of value R 1 and a component 103 comprising a resistor 104 of value R 2 and a capacitor 105 of capacity C 2 in parallel.
  • R 1 corresponds to the internal resistance of the cell
  • R 2 and C 2 are used for modeling frequency phenomena inside the cell.
  • the voltage across the terminals of the component 103 is hereafter referenced Uc 2 -.
  • the voltage across the terminals of the closed-circuit model circuit is the voltage u_cell across the terminals of the corresponding cell.
  • the open circuit voltage across the terminals of this model circuit is the voltage OCV which corresponds to the open circuit voltage of the cell.
  • the current passing through this model circuit is the current passing through the corresponding cell i_cell.
  • the values R 1 , R 2 of the resistors 102 , 104 and the value C 2 of the capacity of the capacitor 105 of the electric model circuit 100 preferably depend on the value temp_cell_est_i of the temperature temps_cell of the cell of index i, and/or on the state of charge SOCcell of the cell modeled by the model circuit in question and/or on the lifetime of the cell.
  • the lifetime of the cell corresponds for example to the time elapsed since its manufacture or to the time elapsed since its putting into service in the electric vehicle. It is a parameter making it possible to quantify the loss of capacity of the cell since the start of its use. In fact, the process of ageing of the cell results in a reduction of the capacity of the cell.
  • This parameter is then used for determining the values R 1 , R 2 of the resistors 102 , 104 and/or the value C 2 of the capacity of the capacitor 105 of the electric model circuit 100 .
  • these values R 1 , R 2 and C 2 constitute parameters of the state observer and are determined by the control unit from pre-established maps as a function of the value temp_cell_est_i of the temperature temp_cell of the cell of index i at the time t_k and as a function of the state of charge of the cell estimated for the time t_k in the preceding calculation step.
  • the value of the temperature temp_cell_est_i can be measured, determined by calculation or estimated from other information on the functioning of the cell.
  • the state observer FK_i associated with the cell of index i therefore accepts as input the value I_cell_mes_i of the input variable i_cell, the value V_cell_mes_i of the output variable u_cell and the value temp_cell_est_i of the temperature temp_cell of the cell of index i, as shown in FIG. 4 .
  • the state observer comprises for example a Kalman filter.
  • This Kalman filter comprises at least one parameter depending on the functioning of the cell, for example on the function relating the open circuit voltage OCV of the cell, which corresponds to the voltage generated by the voltage generator 101 in the model circuit 100 , and the state of charge SOCcell of the cell.
  • the open circuit voltage OCV of a cell is a non-linear function of its state of charge SOCcell, and different for each cell chemistry.
  • An example is given in FIG. 3 in solid line.
  • OCV(SOCcell) a ⁇ SOCcell+b, where a and b are two characteristic parameters of the cell and of the range of states of charge considered.
  • the total capacity in amp-hours (referenced Ah) of the cell modeled by the model circuit 100 is referenced Q max .
  • the total capacity is an intrinsic characteristic of each cell and depends on the temperature of the cell and on its lifetime.
  • the cells of a battery have similar, but not necessarily identical, capacities.
  • ⁇ ⁇ x k [ SOCcell k U C ⁇ ⁇ 2 , k ]
  • y k u_cell k - b
  • u k I bat
  • a s [ 1 0 0 ( 1 - Te R ⁇ ⁇ 2 ⁇ C ⁇ ⁇ 2 ) ]
  • B s [ Te Q max Te C ⁇ ⁇ 2 ]
  • C s [ a 1 ] ,
  • U k represents the input variable of the Kalman filter, that is to say in this case the current at the terminals of the cell which is equal to the current at the terminals of the battery to which is possibly added the balancing current of the cell when a balancing system is used
  • X k represents the state of the system, that is to say in this case the state of charge of the cell and the voltage U C2 across the terminals of the component 103 and y k represents the output variable.
  • This output variable gives access to an estimated value u_cell_est_i of the voltage across the terminals of the cell of index i at the time k.
  • the matrices A s , B s and D s are updated at each calculation step, that is to say at each time t_k, since they depend on the parameters R 1 , R 2 and C 2 , which vary as a function of the value temps_cell_est_i of the temperature temp_cell of the cell and of the state of charge SOCcell of that cell.
  • step a) During the carrying out of step a) by using the Kalman filter, firstly there is made an estimation of the values of the state and output variables of the Kalman filter. In order to do this, the predicted state at the time t (k+1) is calculated as a function of the state at the time t_k, by means of the characteristic equations of the use of the Kalman filter:
  • ⁇ circumflex over (x) ⁇ k+1 ⁇ k A s ⁇ circumflex over (x) ⁇ k ⁇ k +B s u k
  • ⁇ k+1 ⁇ k C s ⁇ circumflex over (x) ⁇ k ⁇ k +D s u k
  • K k+1 P k+1 ⁇ k C s T ( C s P k+1 ⁇ k C s T +R kal ) ⁇ 1
  • P k+1 ⁇ k is the matrix of predicted estimation of the covariance of the error in the predicted state and P k+1 ⁇ k+1 is the matrix of a posteriori estimation of the covariance of this error.
  • the predicted state ⁇ circumflex over (x) ⁇ k+1 ⁇ k is corrected as a function of the error in the estimated output, that is to say as a function of the difference between the measured value of the output variable y k+ i and the predicted value ⁇ i+1 ⁇ i of that output, by carrying out the following calculation:
  • ⁇ circumflex over (x) ⁇ k+1 ⁇ k+1 ⁇ circumflex over (x) ⁇ k+1 ⁇ k K k+1 ( y k+1 ⁇ k+1 ⁇ k ).
  • the Kalman filter thus gives access to an estimated value SOCcell_est_i of the state of charge SOCcell of the cell of index i and to an estimated value u_cell_est_i of the voltage u_cell across the terminals of the cell of index i.
  • This estimated value u_cell_est_i of the voltage across the terminals of the cell is equal to ⁇ i+1 ⁇ i +b (see FIGS. 1 and 4 ).
  • the rate of growth referenced “a” of the affine function relating the open circuit voltage OCV and the state of charge of the cell is a parameter of the Kalman filter used for determining the state of charge of the cell.
  • a different Kalman filter is therefore used for each range of value of the state of charge of the cell associated with a different affine part of the function relating the open circuit voltage OCV of the cell and the state of charge of the cell.
  • the Kalman filter used during the implementation of step a) at a given time t_k is therefore determined as a function of the value of the state of charge of the cell estimated at the preceding time t_(k ⁇ 1 ).
  • the curve representing the open circuit voltage OCV of the cell as a function of its state of charge is approximated by four different affine zones, respectively corresponding to a range of state of charge values between 0 and 10%, 10 and 30%, 30 and 90%, 90 and 100%.
  • a different Kalman filter is therefore used for each of these ranges of state of charge values of the cell in question.
  • Kalman filters are activated alternately.
  • An example of use of these different filters is shown in FIG. 4 , for a cell of index i.
  • the Kalman filter FK_i corresponding to this cell of index i comprises four Kalman filters FK_i1, FK_i2, FK_i3 and FK_i4. Each of these filters is adapted to receive on its input the values of the voltage across the terminals of the cell of index i, of the current passing through this cell and of the temperature of the cell, as well as the values of the state and output variables estimated at the preceding time.
  • block 15 of FIG. 4 accepts at its input the value of the vector X k estimated at the time k and at its output gives the value of the vector X k+ i estimated at the preceding time.
  • the vector X 0 of initialization at the time t0 is the vector (SOC_ini, 0), where SOC_ini is the initialization value of the calculation.
  • the transition between two ranges of values, and therefore between two different Kalman filters, is managed by an automated system A comprising hystereses so as to prevent oscillation between two Kalman filters.
  • This automated system A accepts on its input the value SOCcell_est_i(k ⁇ 1) determined at the time preceding the calculation in progress ( FIG. 4 , block 16 shows that only the value of the first coordinate of the vector is used) for the cell of index i in question.
  • the automated system A transmits, on its output, a signal to activate one of the Kalman filters FK_i1, FK_i2, FK_i3, FK_i4, respectively referenced Ac1x, Ac2, Ac3 or Ac4, according to whether the value of the state of charge at the preceding time SOCcell_est_i(k ⁇ 1) is included in one or other of the four ranges of values of states of charge defined above.
  • the transition between two Kalman filters corresponding to two ranges of state of charge values is initiated when the state of charge of the corresponding cell reaches a threshold value which is different according to the direction of variation of that state of charge at that time.
  • the move from a first Kalman filter corresponding to a first range of state of charge values to a second Kalman filter corresponding to a second range of state of charge values occurs when the estimated value of the state of charge of the cell reaches a first threshold if the state of charge is increasing.
  • the return from the second Kalman filter to the first Kalman filter occurs when the estimated value of the state of charge reaches a second threshold, different from the first threshold, if the state of charge is decreasing.
  • the second threshold is preferably lower than the first threshold.
  • the transition between the filter FK_it corresponding to the first range and the one corresponding to the second range FK_i2 is initiated when the estimated state of charge value increases to reach 10%.
  • the return to the first filter FK_i1 is not initiated when the estimated state of charge value reduces and goes below 10%, but rather when it reduces and reaches 9% for example, that is to say a threshold value lower than 10%.
  • the Kalman filter is initialized with the previously calculated state of charge value SOCcellest_i(k ⁇ 1) in order to guarantee a smooth transition and therefore a continuous variation of the estimated state of charge value.
  • each Kalman filter FK_i, and FK_i1, FK_i2, FK_i3, FK_i4 if several filters are used for a same cell are adjusted independently for each range of state of charge values.
  • step a) the state of charge of each cell is determined by an amp-hour-metric metering method, that is to say by metering amp-hours.
  • the battery in question is a traction battery of an electric or hybrid motor vehicle.
  • the control unit is then incorporated in the control unit of the vehicle and receives the information transmitted by the various sensors of that vehicle.
  • step c) the state of charge is determined according to step c) as described above.
  • the method according to the invention makes it possible to obtain a battery state of charge value equal to 100% when the state of charge of the most charged cell is 100%, a state of charge value equal to 0% when the state of charge of the least charged cell is 0%, and a continuous and representative variation of the state of charge of the battery between these two extreme values.
  • FIG. 5 shows the variation as a function of time of the state of charge of the battery (solid line) estimated according to the method and the state of charge of several cells (dotted lines) for a cycle of discharging the battery.
  • the method described here can be used for a battery comprising a balancing system the purpose of which is to allow the use of the battery over a maximum range of use in order to increase the autonomy of the vehicle.
  • a balancing system the purpose of which is to allow the use of the battery over a maximum range of use in order to increase the autonomy of the vehicle.
  • step a) it is possible to increase the order of the Kalman filter used, for example to use a third order filter. It is also possible to consider the use of an adaptive observer which estimates the parameters R 1 , R 2 and C 2 at each calculation step instead of using values coming from maps.
  • the method is used in the presence of a balancing system, it is possible to take account of the efficiency of the balancing system in order to correct the estimated value of the state of charge of the battery. In this case, the estimated state of charge of the battery will be higher from the start of the balancing.
  • the method has a particularly advantageous application for estimating the state of charge of the traction battery of an electric or hybrid motor vehicle.
  • the state of charge of the battery information is displayed to the driver by the intermediary of a battery gauge on the dashboard.
  • the driver must be able to base himself on this information in order to make decisions relating to the driving of the vehicle in total safety. It is notably important to provide him with accurate information when the state of charge is low, in order to prevent him from experiencing a loss of energy or a lack of motor power.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
US14/119,126 2011-05-20 2012-05-16 Method of estimating the state of charge of an electric battery Abandoned US20140203813A1 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
FR1101568 2011-05-20
FR1101568A FR2975501B1 (fr) 2011-05-20 2011-05-20 Procede d'estimation de l'etat de charge d'une batterie electrique
PCT/FR2012/051116 WO2012160301A1 (fr) 2011-05-20 2012-05-16 Procede d'estimation de l'etat de charge d'une batterie electrique

Publications (1)

Publication Number Publication Date
US20140203813A1 true US20140203813A1 (en) 2014-07-24

Family

ID=46321143

Family Applications (1)

Application Number Title Priority Date Filing Date
US14/119,126 Abandoned US20140203813A1 (en) 2011-05-20 2012-05-16 Method of estimating the state of charge of an electric battery

Country Status (7)

Country Link
US (1) US20140203813A1 (ko)
EP (1) EP2710392B1 (ko)
JP (1) JP6249942B2 (ko)
KR (1) KR101902529B1 (ko)
CN (1) CN103688181B (ko)
FR (1) FR2975501B1 (ko)
WO (1) WO2012160301A1 (ko)

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150231985A1 (en) * 2014-02-20 2015-08-20 Ford Global Technologies, Llc State of Charge Quality Based Cell Balancing Control
US20160016482A1 (en) * 2014-07-17 2016-01-21 Ford Global Technologies, Llc Real-time battery estimation
US20160124051A1 (en) * 2014-10-29 2016-05-05 Texas Instruments Incorporated Battery fuel gauge
US9539912B2 (en) 2014-02-20 2017-01-10 Ford Global Technologies, Llc Battery capacity estimation using state of charge initialization-on-the-fly concept
KR20170138488A (ko) * 2015-04-16 2017-12-15 옥시스 에너지 리미티드 리튬 유황 배터리의 충전 상태 및 건강 상태를 결정하는 방법 및 장치
CN107487199A (zh) * 2016-09-28 2017-12-19 宝沃汽车（中国）有限公司 电池管理系统及电动车电池的剩余电量的处理方法
CN107487198A (zh) * 2016-09-28 2017-12-19 宝沃汽车（中国）有限公司 电动车电池的剩余电量的处理方法、装置及电动车
US10048319B2 (en) 2012-05-11 2018-08-14 Renault S.A.S. Estimating of the state of charge of a battery
US20200028217A1 (en) * 2018-07-19 2020-01-23 Ford Global Technologies, Llc Traction battery with cell zone monitoring
US10663523B2 (en) 2014-11-18 2020-05-26 The Ritsumeikan Trust Remaining stored power amount estimation device, method for estimating remaining stored power amount of storage battery, and computer program
CN111435150A (zh) * 2019-01-11 2020-07-21 通用汽车环球科技运作有限责任公司 使用电芯组电荷状态变化范围估计剩余电池组能量的方法和系统
WO2020227550A1 (en) * 2019-05-08 2020-11-12 The Texas A&M University System Smart hydrogen storage protocol
WO2021073690A3 (de) * 2019-10-15 2021-06-10 Hochschule Offenburg Verfahren und vorrichtung zur bestimmung des ladezustandes und des gesundheitszustandes einer aufladbaren batterie
US11378624B2 (en) * 2017-10-31 2022-07-05 Samsung Sdi Co., Ltd. Method for displaying charge amount of battery, and battery pack and electronic device for performing same

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3010797B1 (fr) * 2013-09-18 2015-10-02 Renault Sa Procede d'estimation du vieillissement d'une cellule de batterie d'accumulateurs
CN103616647B (zh) * 2013-12-09 2016-03-02 天津大学 一种用于电动汽车电池管理系统的电池剩余电量估计方法
FR3015048B1 (fr) * 2013-12-12 2015-12-18 Renault Sas Evaluation de la quantite d'energie dans une batterie de vehicule automobile
EP3149500B1 (en) * 2014-05-28 2017-12-20 Volvo Truck Corporation Method for determining the reliability of state of health parameter values
FR3029296B1 (fr) * 2014-11-28 2016-12-30 Renault Sa Procede automatique d'estimation de l'etat de charge d'une cellule d'une batterie
FR3029298B1 (fr) * 2014-11-28 2016-12-30 Renault Sa Procede automatique d'estimation de l'etat de charge d'une cellule d'une batterie
KR101628564B1 (ko) * 2014-12-09 2016-06-21 현대자동차주식회사 하이브리드 차량의 배터리 soc 리셋 방법
CN109917298A (zh) * 2017-12-13 2019-06-21 北京创昱科技有限公司 一种电池荷电状态预测方法和系统
CN111180811B (zh) * 2020-01-06 2022-03-25 宁波吉利汽车研究开发有限公司 一种车用动力蓄电池组实际soc的获取方法及装置
CN114002603B (zh) * 2020-07-13 2022-08-02 宁德时代新能源科技股份有限公司 识别电芯的方法、装置及计算机存储介质
CN112083336B (zh) * 2020-10-19 2022-02-01 哈尔滨工业大学（威海） 一种锂离子电池组电化学模型参数获取方法
CN114035059B (zh) * 2021-11-08 2024-07-16 东软睿驰汽车技术（沈阳）有限公司 显示soc精度的计算方法、装置和电子设备
KR20240023762A (ko) * 2022-08-16 2024-02-23 주식회사 엘지에너지솔루션 이차 전지의 충방전 거동의 모사 방법

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120091969A1 (en) * 2009-06-24 2012-04-19 Toyota Jidosha Kabushiki Kaisha Upper-limit of state-of-charge estimating device and upper-limit of state-of-charge estimating method
US20120179435A1 (en) * 2011-01-10 2012-07-12 Ford Global Technologies, Llc Method For Determining A Power Capability For A Battery

Family Cites Families (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3533076B2 (ja) * 1997-10-13 2004-05-31 トヨタ自動車株式会社 組電池の蓄電状態検出方法、検出装置、および組電池の充放電制御装置
JP3921826B2 (ja) * 1998-07-22 2007-05-30 日産自動車株式会社 組電池セル容量調整方法
JP2004014205A (ja) * 2002-06-05 2004-01-15 Nissan Motor Co Ltd 電池異常劣化検出装置
JP2004271410A (ja) * 2003-03-11 2004-09-30 Hitachi Ltd 電気車のバッテリ制御装置
JP3997965B2 (ja) * 2003-07-29 2007-10-24 トヨタ自動車株式会社 組電池の充放電制御装置および方法、プログラム、電池制御システム
US7109685B2 (en) * 2003-09-17 2006-09-19 General Motors Corporation Method for estimating states and parameters of an electrochemical cell
US7321220B2 (en) * 2003-11-20 2008-01-22 Lg Chem, Ltd. Method for calculating power capability of battery packs using advanced cell model predictive techniques
DE102005020835A1 (de) * 2004-05-13 2006-03-09 Robert Bosch Gmbh Batteriezustandserkennung
US7525285B2 (en) * 2004-11-11 2009-04-28 Lg Chem, Ltd. Method and system for cell equalization using state of charge
KR100793616B1 (ko) * 2005-06-13 2008-01-10 주식회사 엘지화학 배터리 잔존량 추정 장치 및 방법
JP4984527B2 (ja) * 2005-12-27 2012-07-25 トヨタ自動車株式会社 二次電池の充電状態推定装置および充電状態推定方法
US7521895B2 (en) * 2006-03-02 2009-04-21 Lg Chem, Ltd. System and method for determining both an estimated battery state vector and an estimated battery parameter vector
KR100804698B1 (ko) * 2006-06-26 2008-02-18 삼성에스디아이 주식회사 배터리 ｓｏｃ 추정 방법 및 이를 이용하는 배터리 관리시스템 및 구동 방법
JP5254568B2 (ja) * 2007-05-16 2013-08-07 日立ビークルエナジー株式会社 セルコントローラ、電池モジュールおよび電源システム
JP4987581B2 (ja) * 2007-06-15 2012-07-25 日立ビークルエナジー株式会社 電池制御装置
JP5009721B2 (ja) 2007-08-24 2012-08-22 プライムアースＥｖエナジー株式会社 二次電池の充電状態推定装置及びプログラム
JP5469813B2 (ja) * 2008-01-29 2014-04-16 株式会社日立製作所 車両用電池システム
CN101526587B (zh) * 2009-03-20 2011-05-04 惠州市亿能电子有限公司 串联电池组荷电状态的测量方法
KR101036061B1 (ko) * 2009-04-21 2011-05-19 에스비리모티브 주식회사 배터리 관리 시스템 및 그 구동 방법
WO2011027449A1 (ja) * 2009-09-03 2011-03-10 トヨタ自動車株式会社 組電池の充電状態検出装置および充電状態検出方法
CN101692120B (zh) * 2009-09-27 2012-07-18 惠州市亿能电子有限公司 一种测量串联蓄电池组最大可用能量的测量方法

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120091969A1 (en) * 2009-06-24 2012-04-19 Toyota Jidosha Kabushiki Kaisha Upper-limit of state-of-charge estimating device and upper-limit of state-of-charge estimating method
US20120179435A1 (en) * 2011-01-10 2012-07-12 Ford Global Technologies, Llc Method For Determining A Power Capability For A Battery

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10048319B2 (en) 2012-05-11 2018-08-14 Renault S.A.S. Estimating of the state of charge of a battery
US9381825B2 (en) * 2014-02-20 2016-07-05 Ford Global Technologies, Llc State of charge quality based cell balancing control
US9539912B2 (en) 2014-02-20 2017-01-10 Ford Global Technologies, Llc Battery capacity estimation using state of charge initialization-on-the-fly concept
US20150231985A1 (en) * 2014-02-20 2015-08-20 Ford Global Technologies, Llc State of Charge Quality Based Cell Balancing Control
US20160016482A1 (en) * 2014-07-17 2016-01-21 Ford Global Technologies, Llc Real-time battery estimation
US9381823B2 (en) * 2014-07-17 2016-07-05 Ford Global Technologies, Llc Real-time battery estimation
US10308129B2 (en) * 2014-07-17 2019-06-04 Ford Global Technologies, Llc Real-time battery estimation
US20160124051A1 (en) * 2014-10-29 2016-05-05 Texas Instruments Incorporated Battery fuel gauge
US9891285B2 (en) * 2014-10-29 2018-02-13 Texas Instruments Incorporated Battery fuel gauge
US10663523B2 (en) 2014-11-18 2020-05-26 The Ritsumeikan Trust Remaining stored power amount estimation device, method for estimating remaining stored power amount of storage battery, and computer program
US20220082631A1 (en) * 2015-04-16 2022-03-17 Oxis Energy Limited Method and apparatus for determining the state of health and state of charge of lithium sulfur batteries
US11125827B2 (en) * 2015-04-16 2021-09-21 Oxis Energy Limited Method and apparatus for determining the state of health and state of charge of lithium sulfur batteries
KR102652848B1 (ko) * 2015-04-16 2024-04-01 겔리온 테크놀로지스 피티와이 리미티드 리튬 유황 배터리의 충전 상태 및 건강 상태를 결정하는 방법 및 장치
KR20170138488A (ko) * 2015-04-16 2017-12-15 옥시스 에너지 리미티드 리튬 유황 배터리의 충전 상태 및 건강 상태를 결정하는 방법 및 장치
US20180095141A1 (en) * 2015-04-16 2018-04-05 Oxis Energy Limited Method and apparatus for determining the state of health and state of charge of lithium sulfur batteries
CN107487198A (zh) * 2016-09-28 2017-12-19 宝沃汽车（中国）有限公司 电动车电池的剩余电量的处理方法、装置及电动车
CN107487199A (zh) * 2016-09-28 2017-12-19 宝沃汽车（中国）有限公司 电池管理系统及电动车电池的剩余电量的处理方法
US11378624B2 (en) * 2017-10-31 2022-07-05 Samsung Sdi Co., Ltd. Method for displaying charge amount of battery, and battery pack and electronic device for performing same
US20200028217A1 (en) * 2018-07-19 2020-01-23 Ford Global Technologies, Llc Traction battery with cell zone monitoring
US10991991B2 (en) * 2018-07-19 2021-04-27 Ford Global Technologies, Llc Traction battery with cell zone monitoring
US11034257B2 (en) * 2019-01-11 2021-06-15 GM Global Technology Operations LLC Method and system for estimating remaining battery pack energy using cell-group state of charge spread
CN111435150A (zh) * 2019-01-11 2020-07-21 通用汽车环球科技运作有限责任公司 使用电芯组电荷状态变化范围估计剩余电池组能量的方法和系统
US20220178499A1 (en) * 2019-05-08 2022-06-09 The Texas A&M University System Smart Hydrogen Storage Protocol
WO2020227550A1 (en) * 2019-05-08 2020-11-12 The Texas A&M University System Smart hydrogen storage protocol
WO2021073690A3 (de) * 2019-10-15 2021-06-10 Hochschule Offenburg Verfahren und vorrichtung zur bestimmung des ladezustandes und des gesundheitszustandes einer aufladbaren batterie
CN114585936A (zh) * 2019-10-15 2022-06-03 本宁Cms技术公司 用于确定充电电池的荷电状态和健康状态的方法和装置
AU2020367926B2 (en) * 2019-10-15 2023-09-14 Benning CMS Technology GmbH Method and device for determining the state of charge and the state of health of a rechargeable battery
US20240295605A1 (en) * 2019-10-15 2024-09-05 Benning CMS Technology GmbH Method and device for determining the state of charge and the state of health of a rechargeable battery

Also Published As

Publication number Publication date
KR101902529B1 (ko) 2018-09-28
JP2014520254A (ja) 2014-08-21
CN103688181A (zh) 2014-03-26
EP2710392B1 (fr) 2018-07-04
CN103688181B (zh) 2016-08-17
JP6249942B2 (ja) 2017-12-20
FR2975501A1 (fr) 2012-11-23
WO2012160301A1 (fr) 2012-11-29
EP2710392A1 (fr) 2014-03-26
FR2975501B1 (fr) 2013-05-31
KR20140034834A (ko) 2014-03-20

Similar Documents

Publication Publication Date Title
US20140203813A1 (en) Method of estimating the state of charge of an electric battery
KR102071685B1 (ko) 충전 상태 사용 범위가 가변적인 복수 개의 셀들을 포함하는 배터리의 충전 상태를 평가하는 방법
CN107402353A (zh) 一种对锂离子电池的荷电状态进行滤波估计的方法及系统
JP5946436B2 (ja) バッテリのパラメータ推定装置及びパラメータ推定方法
EP3438683B1 (en) Secondary battery deterioration estimation device and secondary battery deterioration estimation method
CN100547849C (zh) 估计电池充电状态的方法、电池管理系统及其驱动方法
US10048319B2 (en) Estimating of the state of charge of a battery
JP6317031B2 (ja) 電池制御装置、および、車両システム
US8645088B2 (en) Systems and methods for determining the state of charge of a battery utilizing confidence values
US10359474B2 (en) Charge state calculation device and charge state calculation method
JP5595361B2 (ja) 二次電池の充電状態推定装置
WO2016067587A1 (ja) バッテリのパラメータ推定装置
US20150260800A1 (en) Device for estimating parameters of battery, and estimation method
US11982720B2 (en) Method and battery management system for ascertaining a state of health of a secondary battery
JP6450565B2 (ja) バッテリのパラメータ推定装置
WO2008066203A1 (fr) Programme d'estimation d'état de charge, système de transport sans fil et procédé de charge
CN113853524B (zh) 用于估计电池状态的方法
CN108037464A (zh) 一种基于imm-ekf的电池组soc估算的方法
CN103718418A (zh) 用于估计电池充电结束时间的系统和方法
CN110333460B (zh) 动力电池组的rc网络参数获取方法、装置和电动汽车
CN113826019A (zh) 用于估计电池状态的方法
WO2021005969A1 (ja) 管理装置、及び車両用電源システム
EP3048450B1 (en) System and method for determining battery usage limits

Legal Events

Date Code Title Description
AS Assignment

Owner name: RENAULT S.A.S., FRANCE

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DRIEMEYER-FRANCO, ANA-LUCIA;REEL/FRAME:032204/0055

Effective date: 20140108

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION